The GHS is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, health and social development, housing, household access to services and facilities, food security, and agriculture.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The units of anaylsis for the General Household Survey 2014 are individuals and households.
v1: Edited, anonymised dataset for public distribution
Version 1 of the General Household Survey 2014 was downloaded from the Statistics South Africa website on the 30th of June 2015.
The 2008-2014 sample was designed to report at Provincial and Metro level (not DC level). However, StatsSA did not take the absent population estimates at metro level into account when weighting the data and therefore this data is not reliable at Metro level. StatsSA's new master sample used from 2015 onwards will weight the data down to metro level and the data will be provided at that level.
The scope of the General Household Survey 2014 includes:
Household characteristics: Dwelling type, home ownership, access to water and sanitation, access to services, transport, household assets, land ownership, agricultural production
Individuals' characteristics: demographic characteristics, relationship to household head, marital status, language, education, employment, income, health, fertility, disability, access to social services, mortality.
LABOUR AND EMPLOYMENT 
DEMOGRAPHY AND POPULATION 
The General Household Survey 2014 had national coverage.
The lowest level of geographic aggregation for this dataset is province.
The lowest level of geographic aggregations covered by the General Household Survey 2011 is Province.
The survey covers all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as student hostels, old age homes, hospitals, prisons and military barracks.
Producers and sponsors
Statistics South Africa
The sample design for the GHS 2014 was based on a master sample (MS) that was originally designed for the Quarterly Labour Force Survey (QLFS) and was used for the first time for the GHS in 2008. This master sample is shared by the QLFS, GHS, Living Conditions Survey (LCS), Domestic Tourism Survey (DTS) and the Income and Expenditure Survey (IES).
The master sample used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of primary sampling units (PSUs) from within strata, and systematic sampling of dwelling units (DUs) from the sampled PSUs. A self-weighting design at provincial level was used and MS stratification was divided into two levels. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the Census 2001 data were summarised at PSU level. The following variables were used for secondary stratification: household size, education, occupancy status, gender, industry and income.
Census enumeration areas (EAs) as delineated for Census 2001 formed the basis of the PSUs. The following additional rules were used:
• Where possible, PSU sizes were kept between 100 and 500 DUs;
• EAs with fewer than 25 DUs were excluded;
• EAs with between 26 and 99 DUs were pooled to form larger PSUs and the criteria used was same settlement type;
• Virtual splits were applied to large PSUs: 500 to 999 split into two; 1 000 to 1 499 split into three; and 1 500 plus split into four PSUs; and
• Informal PSUs were segmented.
A randomised-probability-proportional-to-size (RPPS) systematic sample of PSUs was drawn in each stratum, with the measure of size being the number of households in the PSU. Altogether approximately 3 080 PSUs were selected. In each selected PSU a systematic sample of dwelling units was drawn. The number of DUs selected per PSU varies from PSU to PSU and depends on the Inverse Sampling Ratios (ISR) of each PSU.
The sampling weights for the data collected from the sampled households were constructed so that the responses could be properly expanded to represent the entire civilian population of South Africa. The design weights, which are the inverse sampling rate (ISR) for the province, are assigned to each of the households in a province. These were adjusted for four factors: Informal PSUs, Growth PSUs, Sample Stabilisation, and Non-responding Units.
Mid-year population estimates produced by the Demographic Analysis division were used for benchmarking. The final survey weights were constructed using regression estimation to calibrate to national level population estimates cross-classified by 5-year age groups, gender and race, and provincial population estimates by broad age groups. The 5-year age groups are: 0–4, 5–9, 10–14, 55–59, 60–64; and 65 and older. The provincial level age groups are 0–14, 15–34, 35–64; and 65 years and older. The calibrated weights were constructed in such away that all persons in a household would have the same final weight.
The Statistics Canada software StatMx was used for constructing calibration weights. The population controls at national and provincial levels were used for the cells defined by crossclassification of Age by Gender by Race. Records for which the age, population group or sex had item non-response could not be weighted and were therefore excluded from the dataset. No imputation was done to retain these records.
Dates of Data Collection
Data Collection Mode
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University of Cape Town
University of Cape Town
Public use data, available to all
Statistics South Africa. General Household Survey 2014 [dataset]. Version 1. Pretoria. Statistics South Africa [producer], 2015. Cape Town. DataFirst [distributor], 2015.
Disclaimer and copyrights
The use of any data is subject to acknowledgement of Stats SA as the supplier and owner of copyright. Statistics South Africa (Stats SA) will not be liable for any damages or losses, except to the extent that such losses or damages are attributable to a breach by Stats SA of its obligations in terms of an existing agreement or to the negligence or wilful act or omissions of the Stats SA, its servants or agents, arising out of the supply of data and or digital products in terms of that agreement. The user indemnifies Stats SA against any claims of whatsoever nature (including legal costs) by third parties arising from the reformatting, restructuring, reprocessing and/or addition of the data, by the user.